Abstract

Machine learning is a means of automatically generating solutions that
perform better than those that are hand-coded by human programmers.
We present a general behavior-based algorithm that uses reinforcement
learning to improve the spatio-temporal organization of a homogeneous
group of robots. In this algorithm each robot applies the
learning at the level of individual behavior selection.
We demonstrate how the interactions within the group affect the
individual learning in a way that produces group-level effects, such
as lane-formation and specialization, and improves the group's
performance. We also present a model of multi-robot task allocation
as resource distribution through vacancy chains, a distribution method
common in human and animal societies, and an algorithm for multi-robot
task allocation based on that model. The model explains and predicts
the task allocation achieved by our algorithm and highlights its
limitations. We present experimental results that validate our model
and show that our algorithm outperforms pre-programmed solutions.
Last, we present an extension of our algorithm that makes it applicable to
heterogeneous groups of robots by making it sensitive to differences
in individual robot performance levels.